Artificial Neural Network System to Predict Golf Score on the PGA Tour

1. Artificial Neural Network System to Predict Golf Score on the PGA Tour ECE 539 – Fall 2003 Final Project Robert Steffes ID: 901-685-8871

2. Idea • Use averages from seven of the major shot categories to predict scoring average. • Inputs include: Driving Distance, Driving Accuracy (%), Greens in Regulation (%), Putting Average, Birdie Average, Sand Saves (%), and Putts per Round. • The MLP is then tested using these inputs and a scoring average is predicted.

3. Implementation • Data gathered from top 188 players on the PGA Tour. • Create training and testing files from this data. • Run through MLP with several tests to get the optimum parameters: 3 Layers, 4 Hidden Neurons, Learning Rate=0.1, Momentum=0.3, 1000 Epochs.

4. Results • 77% average classification rate on multiple tests run. Compare to 17% random classification. • No similar system implemented yet. • Wide range of applications if used on the PGA Tour.

5. Conclusion • All players have access to their shot trends, averages, and statistics, but it is virtually impossible to draw a correlation just by looking at them. • Potential applications beyond simply forecasting a player’s score • Eg. A player may hypothetically change one of his statistics and see whether the MLP predicts that that will change his scoring average

6. Conclusion • Professionals looking for every advantage they can get • A system to analyze their statistics and predict their scores could be extremely valuable if utilized • I would like to look into actually developing a product from the concept of this project in the future